CN117556773A - Filter optimization method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application provides a method and a device for optimizing a filter, electronic equipment and a storage medium, wherein the method comprises the following steps: according to the initial value of each parameter to be adjusted of the filter and n groups of linearly independent setting values of the parameter to be adjusted, determining an initial coupling matrix and n first coupling matrices, and constructing an initial data set; carrying out Taylor expansion processing according to the initial data set, determining a first-order Taylor expansion coefficient by utilizing the initial data set, and constructing a reduced-order model; performing parameter optimization by using a reduced order model to obtain estimated optimal values of all parameters to be adjusted; according to the estimated optimal values of all parameters to be adjusted, obtaining the current S parameter of the filter through simulation and determining the current coupling matrix according to the current S parameter; determining whether the optimization meets the standard or not; if yes, determining the current estimated optimal value as a target value, and ending. Therefore, by constructing the reduced order model, the original model is replaced to participate in optimization operation, and the simulation times are reduced, so that the optimization efficiency can be effectively improved.
Description
Technical Field
The present disclosure relates to the field of microwave filters, and in particular, to a method and apparatus for optimizing a filter, an electronic device, and a storage medium.
Background
Microwave filters are widely used in base stations for filtering signals outside the desired frequency band, and because of their sensitivity to size and sensitivity, the filters cannot be manually dimensioned during design. The design of the method often needs to determine a rough size, adjust the response of the S parameter (Scattering parameters) to be close to the standard, and optimize the size by using an optimization algorithm to completely reach the standard.
Most of the existing optimization methods, such as Newton optimization method, gradient optimization method, space mapping and the like, have two problems of dependence on initial values and low efficiency. The dependence on the initial value is reflected in that each resonant frequency of the filter to be modulated is required to be located in a simulation frequency band, and the closer the resonant frequency is to the target resonant frequency, the larger the probability of algorithm convergence is. One of the reasons for this is that the more severe the detuning, the more local minima are encountered during the optimization. The second reason is that the more severe the detuning, the greater the error of the coupling matrix. Inefficiency is manifested in that multiple simulations are performed for each iteration of the algorithm, which consumes a significant amount of time. In recent years, many scholars propose to use neural networks for optimization, but the process of acquiring training data by the method is time-consuming, and has the problem of low overall efficiency.
Disclosure of Invention
In view of this, an object of the present application is to provide a method, an apparatus, an electronic device, and a storage medium for optimizing a filter, which are capable of reducing the number of simulations by constructing a reduced order model instead of an original model, thereby improving the optimization efficiency without depending on the original value.
The embodiment of the application provides an optimization method of a filter, which comprises the following steps:
determining an initial coupling matrix and n first coupling matrices according to the initial value of each parameter to be adjusted of the filter and n groups of linearly independent setting values of the parameter to be adjusted, and constructing an initial data set by using the initial value of each parameter to be adjusted, the n groups of linearly independent setting values, the initial coupling matrix and the n first coupling matrices; wherein n is the dimension of the parameter to be adjusted;
performing taylor expansion processing on all elements in the initial coupling matrix and the n first coupling matrices, determining a coefficient of first-order taylor expansion by using the initial data set, and constructing a reduced-order model;
performing parameter optimization by using the reduced order model to obtain an estimated optimal value of the parameters to be adjusted of the filter;
simulating to obtain the current S parameter of the filter according to the estimated optimal value of the parameter to be adjusted of the filter, and extracting the current coupling matrix according to the current S parameter;
Determining whether the optimization meets the standard or not according to the current S parameter, the current coupling matrix, the current target matrix and a preset target return loss;
if yes, determining the estimated optimal value as a target value, and outputting and ending optimization.
Optionally, the determining an initial coupling matrix and n first coupling matrices according to the initial value of each parameter to be adjusted of the filter and n groups of linearly independent setting values of the parameter to be adjusted includes:
according to the initial value of each parameter to be adjusted of the filter, determining the initial S parameter of the filter;
determining the initial coupling matrix according to the initial S parameter;
for n groups of linearly independent set values, determining the first S parameters corresponding to each set of the set values and the first coupling matrixes corresponding to the first S parameters respectively to obtain n first coupling matrixes.
Optionally, the performing parameter optimization by using the reduced order model to obtain an estimated optimal value of the parameter to be adjusted of the filter includes:
constructing a penalty function according to the n groups of linearly independent setting values, and introducing the penalty function into the reduced order model;
and carrying out minimum value solving processing on the reduced order model introduced with the penalty function by using a Gill-Murray Newton method, and determining the estimated optimal value of the parameters to be regulated of the filter according to the solved minimum value.
Optionally, the simulating to obtain the current S parameter of the filter according to the estimated optimal value of the parameter to be adjusted of the filter, and extracting the current coupling matrix according to the current S parameter includes:
inputting the estimated optimal value of the parameters to be adjusted of the filter into a solver, and determining the current S parameter of the filter;
sequentially removing loading phases and calculating remainder to the current S parameters to determine a transverse topology matrix;
sequentially performing element elimination treatment on the transverse topology matrix, determining a wheel type topology matrix, and extracting a transmission zero point;
and performing similar transformation on the wheel type topology matrix according to the transmission zero point to obtain a current coupling matrix corresponding to the actual topology.
Optionally, the current target matrix is determined by:
determining a target S parameter according to a preset target return loss, a preset target transmission zero point and a preset target passband range, and determining a preset target coupling matrix according to the target S parameter;
comparing the current coupling matrix with an actual topology to determine whether parasitic coupling exists in the coupling matrix but does not exist in the actual topology;
if the parasitic coupling exists, adjusting the preset target coupling matrix according to a preset adjustment mode, and determining the current target matrix;
And if the current target matrix does not exist, determining the preset target coupling matrix as the current target matrix.
Optionally, the determining whether the optimization meets the standard according to the current S parameter, the current coupling matrix, the current target matrix, and the preset target return loss includes:
determining a response error according to the current S parameter and the preset target return loss;
determining a matrix error according to the current coupling matrix and the current target matrix;
performing weighted summation processing according to the response error and the matrix error to determine a total error;
if the total error is smaller than the error threshold, determining that the optimization reaches the standard, otherwise, determining that the optimization does not reach the standard.
Optionally, if the standard is not met, the optimization method further includes:
adding the estimated optimal value and the corresponding coupling matrix to the initial data set;
changing a first-order Taylor expansion into a second-order Taylor expansion according to the initial data set, and correcting the coefficient of the Taylor expansion by utilizing the initial data set so as to update the reduced model;
and re-optimizing parameters of the filter by using the updated reduced order model.
The embodiment of the application also provides an optimizing device of the filter, which comprises:
The first construction module is used for determining an initial coupling matrix and n first coupling matrices according to the initial value of each parameter to be regulated of the filter and n groups of linearly independent setting values of the parameter to be regulated, and constructing an initial data set by using the initial value of each parameter to be regulated, the n groups of linearly independent setting values, the initial coupling matrix and the n first coupling matrices; wherein n is the dimension of the parameter to be adjusted;
the second construction module is used for carrying out Taylor expansion processing on all elements in the initial coupling matrix and the n first coupling matrices, determining a first-order Taylor expansion coefficient by utilizing the initial data set and constructing a reduced-order model;
the optimization module is used for carrying out parameter optimization by utilizing the reduced order model to obtain an estimated optimal value of the parameters to be adjusted of the filter;
the simulation module is used for obtaining the current S parameter of the filter through simulation according to the estimated optimal value of the parameter to be adjusted of the filter, and extracting the current coupling matrix according to the current S parameter;
the judging module is used for determining whether the optimization meets the standard or not according to the current S parameter, the current coupling matrix, the current target matrix and the preset target return loss;
And the ending module is used for determining the estimated optimal value as a target value if the estimated optimal value reaches the standard, and outputting and ending the optimization.
Optionally, when the first construction module is configured to determine an initial coupling matrix and n first coupling matrices according to an initial value of each parameter to be adjusted of the filter and n sets of linearly independent setting values of the parameter to be adjusted, the first construction module is configured to:
according to the initial value of each parameter to be adjusted of the filter, determining the initial S parameter of the filter;
determining the initial coupling matrix according to the initial S parameter;
for n groups of linearly independent set values, determining the first S parameters corresponding to each set of the set values and the first coupling matrixes corresponding to the first S parameters respectively to obtain n first coupling matrixes.
Optionally, when the optimizing module is configured to perform parameter optimization by using the reduced order model to obtain an estimated optimal value of the parameter to be adjusted of the filter, the optimizing module is configured to:
constructing a penalty function according to the n groups of linearly independent setting values, and introducing the penalty function into the reduced order model;
and carrying out minimum value solving processing on the reduced order model introduced with the penalty function by using a Gill-Murray Newton method, and determining the estimated optimal value of the parameters to be regulated of the filter according to the solved minimum value.
Optionally, when the simulation module is configured to obtain, in a simulation manner, a current S parameter of the filter according to an estimated optimal value of a parameter to be adjusted of the filter, and extract a current coupling matrix according to the current S parameter, the simulation module is configured to:
inputting the estimated optimal value of the parameters to be adjusted of the filter into a solver, and determining the current S parameter of the filter;
sequentially removing loading phases and calculating remainder to the current S parameters to determine a transverse topology matrix;
sequentially performing element elimination treatment on the transverse topology matrix, determining a wheel type topology matrix, and extracting a transmission zero point;
and performing similar transformation on the wheel type topology matrix according to the transmission zero point to obtain a current coupling matrix corresponding to the actual topology.
Optionally, the optimizing device further includes a determining module, where the determining module is configured to:
determining a target S parameter according to a preset target return loss, a preset target transmission zero point and a preset target passband range, and determining a preset target coupling matrix according to the target S parameter;
comparing the current coupling matrix with an actual topology to determine whether parasitic coupling exists in the coupling matrix but does not exist in the actual topology;
If the parasitic coupling exists, adjusting the preset target coupling matrix according to a preset adjustment mode, and determining the current target matrix;
and if the current target matrix does not exist, determining the preset target coupling matrix as the current target matrix.
Optionally, when the judging module is configured to determine whether the current optimization meets the standard according to the current S parameter, the current coupling matrix, the current target matrix, and the preset target return loss, the judging module is configured to:
determining a response error according to the current S parameter and the preset target return loss;
determining a matrix error according to the current coupling matrix and the current target matrix;
performing weighted summation processing according to the response error and the matrix error to determine a total error;
if the total error is smaller than the error threshold, determining that the optimization reaches the standard, otherwise, determining that the optimization does not reach the standard.
Optionally, the optimizing device further includes an updating module, where the updating module is configured to:
adding the estimated optimal value and the corresponding coupling matrix to the initial data set;
changing a first-order Taylor expansion into a second-order Taylor expansion according to the initial data set, and correcting the coefficient of the Taylor expansion by utilizing the initial data set so as to update the reduced model;
And re-optimizing parameters of the filter by using the updated reduced order model.
The embodiment of the application also provides electronic equipment, which comprises: a processor, a memory and a bus, said memory storing machine readable instructions executable by said processor, said processor and said memory communicating over the bus when the electronic device is running, said machine readable instructions when executed by said processor performing the steps of the optimization method as described above.
Embodiments of the present application also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the optimization method as described above.
The embodiment of the application provides a method, a device, electronic equipment and a storage medium for optimizing a filter, wherein the method for optimizing the filter comprises the following steps: determining an initial coupling matrix and n first coupling matrices according to the initial value of each parameter to be adjusted of the filter and n groups of linearly independent setting values of the parameter to be adjusted, and constructing an initial data set by using the initial value of each parameter to be adjusted, the n groups of linearly independent setting values, the initial coupling matrix and the n first coupling matrices; performing taylor expansion processing on all elements in the initial coupling matrix and the n first coupling matrices, determining a coefficient of first-order taylor expansion by using the initial data set, and constructing a reduced-order model; performing parameter optimization by using the reduced order model to obtain an estimated optimal value of the parameters to be adjusted of the filter; simulating to obtain the current S parameter of the filter according to the estimated optimal value of the parameter to be adjusted of the filter, and extracting the current coupling matrix according to the current S parameter; determining whether the optimization meets the standard or not according to the current S parameter, the current coupling matrix, the current target matrix and a preset target return loss; if yes, determining the estimated optimal value as a target value, and outputting and ending optimization.
Therefore, the reduced order model constructed by the method and the device can effectively reduce the simulation times by introducing a penalty function and considering parasitic coupling in optimization, and further provide the optimization efficiency of the filter parameters.
In order to make the above objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered limiting the scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for optimizing a filter according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a filter provided in the present application;
FIG. 3 is a flowchart of a current target coupling matrix determination process provided in the present application;
FIG. 4 is a schematic diagram of a filter response result provided in the present application;
Fig. 5 is a schematic structural diagram of an optimizing device of a filter according to an embodiment of the present application;
FIG. 6 is a second schematic structural diagram of an optimizing device of a filter according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. Based on the embodiments of the present application, every other embodiment that a person skilled in the art would obtain without making any inventive effort is within the scope of protection of the present application.
Microwave filters are widely used in base stations for filtering signals outside the desired frequency band, and because of their sensitivity to size and sensitivity, the filters cannot be manually dimensioned during design. The design of the method often needs to determine a rough size, adjust the response of the S parameter (Scattering parameters) to be close to the standard, and optimize the size by using an optimization algorithm to completely reach the standard.
Most of the existing optimization methods, such as Newton optimization method, gradient optimization method, space mapping and the like, have two problems of dependence on initial values and low efficiency. The dependence on the initial value is reflected in that each resonant frequency of the filter to be modulated is required to be located in a simulation frequency band, and the closer the resonant frequency is to the target resonant frequency, the larger the probability of algorithm convergence is. One of the reasons for this is that the more severe the detuning, the more local minima are encountered during the optimization. The second reason is that the more severe the detuning, the greater the error of the coupling matrix. Inefficiency is manifested in that multiple simulations are performed for each iteration of the algorithm, which consumes a significant amount of time. In recent years, many scholars propose to use neural networks for optimization, but the process of acquiring training data by the method is time-consuming, and has the problem of low overall efficiency.
Based on the above, the embodiment of the application provides a method, a device, an electronic device and a storage medium for optimizing a filter, and by constructing a reduced order model, the simulation times are reduced, so that the optimization efficiency is improved.
Referring to fig. 1, fig. 1 is a flowchart of a method for optimizing a filter according to an embodiment of the present application.
A filter is a device used to change the frequency characteristics of a signal. In electronic circuits, filters are often used to reject noise or certain frequency components in a signal, or to select certain specific frequency components in a signal, and optimization of the filter is one of the important parts in the electronic circuit design. For example, referring to fig. 2, fig. 2 is a schematic structural diagram of a filter provided in the present application. As shown in fig. 2, the appearance and internal structure of the filter with multiple orientations are provided, and it should be noted that, as shown in fig. 2, the change of the dimensions of each place of the filter causes the change of the response result, and in particular, as shown in the figure, some physical parameters are marked, which can be used as variables to be optimized, so that the optimization of the filter can be a set of dimensional variables, so that the response result of the filter can reach the standard under the physical dimensions.
The response result may generally be that the return loss (S11) is smaller than AdB in a certain frequency band, and the transmission loss (S21) is smaller than BdB in a certain frequency band.
With continued reference to fig. 1, as shown in fig. 1, the optimization method provided in the embodiment of the present application includes:
s101, determining an initial coupling matrix and n first coupling matrices according to the initial value of each parameter to be adjusted of the filter and n groups of n linearly independent setting values of the parameter to be adjusted, and constructing an initial data set by using the initial value of each parameter to be adjusted, the n linearly independent setting values, the initial coupling matrix and the n first coupling matrices.
In the step, initial values of all parameters to be adjusted of the filter and n groups of linearly independent setting values are obtained in advance, then an initial coupling matrix is determined according to the initial values of all parameters to be adjusted of the filter, n first coupling matrices are determined according to the n groups of linearly independent setting values of the parameters to be adjusted of the filter, and finally an initial data set is constructed by using the initial values of all parameters to be adjusted and the n groups of linearly independent setting values, the initial coupling matrix and the n first coupling matrices. n is the dimension of the parameter to be adjusted.
By way of example, with continued reference to fig. 2, the parameters to be adjusted may include: the width of the 4 coupling cavities, the depth of the 8 coupling screws and the coupling height of the 2 ports can determine 14 parameters to be adjusted.
It should be noted that, because the S parameter has a plurality of frequency points, each frequency point is a complex function related to the physical size, and the coupling matrix can show the relationship between the response and the physical size more clearly, so that the optimization can be from making the S parameter reach the standard to making the coupling matrix reach the standard.
In one embodiment provided in the present application, the determining an initial coupling matrix and n first coupling matrices according to the initial value of each parameter to be adjusted of the filter and n sets of linearly independent setting values of the parameter to be adjusted includes:
s1011, determining initial S parameters of the filter according to initial values of each parameter to be adjusted of the filter.
S1012, determining the initial coupling matrix according to the initial S parameter.
S1013, aiming at n groups of linearly independent set values, respectively determining the first S parameters corresponding to each group and the first coupling matrixes corresponding to the first S parameters according to the set values of each group, so as to obtain n first coupling matrixes.
For step S1011 and step S1013, the initial response result may be determined by a pre-built simulation model or a solver of TF-eMag, for example.
For step S1012, when determining the initial coupling matrix according to the initial S parameter, the determination may be performed according to a predetermined correspondence between the S parameter and the coupling matrix.
S102, performing Taylor expansion processing on all elements in the initial coupling matrix and the n first coupling matrices, determining a first-order Taylor expansion coefficient by using the initial data set, and constructing a reduced-order model.
By way of example, the construction process of the reduced order model is described by:
obtaining n+1 candidate coupling matrices according to an initial coupling matrix and N first coupling matrices, and then constructing N-dimension approximate Taylor expansion according to the existing data (wherein N is the dimension of the coupling matrix, N is related to the order of a preset filter, N is the dimension of an input variable, namely the number of parameters to be adjusted):
in the above formula, x is an N-dimensional array, M has N x N numbers, and the superscript (i, j) indicates the position in the coupling matrix, i.e. the Taylor expansion is performed on each element in the coupling matrix, M in the formula 0 Representing the initial coupling matrix.
The higher-order terms are then discarded, and expanded as follows:
（2）
and then further expanding according to different positions and sampling points:
（3）
（4）
（5）
（6）
the second order approximation is expanded into a set of linear equations by the above-described decomposition, the solution a of the above-described equation corresponds to the gradient and the solution b corresponds to the black matrix. Knowing the gradient and the black matrix, the approximate expression of the coupling element with respect to x is known, which can be directly optimized at this time, but the relationship is known to be approximate, expressed in two points: 1. when the data quantity is insufficient, according to the approximation focusing on linearity, namely when the number of equations is less than the number of unknowns (namely n (n+1)/2+n), the np-n elements in the black plug matrix to the n (n+1)/2 elements are all set to zero, and the rest elements are obtained. 2. When np > n, (n+1)/2+n, the least squares solution is taken.
Wherein the initial data set is used to determine the coefficients of the first-order taylor expansion, and the values of the coefficients a1 to an can be determined.
And S103, performing parameter optimization by using the reduced order model to obtain an estimated optimal value of the parameters to be adjusted of the filter.
In the step, parameter optimization is carried out by utilizing a Gill-Murray modified Newton method according to the reduced order model, and the estimated optimal values of all parameters to be adjusted of the filter are obtained.
Here, the estimated optimal value may be a value determined by optimizing an initial value, or may be a value to be combined with the initial value.
In an embodiment provided in the present application, the performing parameter optimization by using the reduced order model to obtain an estimated optimal value of a parameter to be adjusted of the filter includes:
s1031, constructing a penalty function according to the n groups of linearly independent setting values, and introducing the penalty function into the reduced order model.
S1032, carrying out minimum value solving processing on the reduced order model introduced with the penalty function by using a Gill-Murray Newton method, and determining the estimated optimal value of the parameters to be adjusted of the filter according to the solved minimum value.
Here, since the model is approximate, a penalty function needs to be added to the original optimization problem during optimization, so that the variation of the parameters to be adjusted is as small as possible, and convergence is realized. Therefore, the calculation process corresponding to the parameter optimization according to the reduced order model is as follows:
（7）
（8）
wherein,for penalty function->Is an error matrix. The current optimum value of all parameters to be adjusted of the filter is calculated, namely +.>。
S104, according to the estimated optimal value of the parameter to be adjusted of the filter, simulating to obtain the current S parameter of the filter, and extracting the current coupling matrix according to the current S parameter.
In this step, the estimated optimal values of all the parameters to be adjusted of the filter may be input into a solution, and processed by a solver to determine the current S parameter of the filter and the current coupling matrix determined according to the current response result.
In one embodiment provided in the present application, the simulating to obtain the current S parameter of the filter according to the estimated optimal value of the parameter to be adjusted of the filter, and extracting the current coupling matrix according to the current S parameter includes:
s1041, inputting the estimated optimal value of the parameter to be adjusted of the filter into a solver, and determining the current S parameter of the filter.
S1042, sequentially removing the loading phase and calculating the remainder of the current S parameter, and determining a transverse topology matrix.
S1043, sequentially performing elimination processing on the transverse topology matrix, determining a round topology matrix, and extracting a transmission zero point.
S1044, performing similar transformation on the wheel type topology matrix according to the transmission zero point to obtain a current coupling matrix corresponding to the actual topology.
When determining the current S parameter of the filter in step S1041, S11, S12, S21, S22 in the S parameter are also determined. S11 is the ratio of the voltage reflected by the port 1 to the incident voltage; s12 is the ratio of the output voltage of the port 1 to the incident voltage of the port 2; s21 is the ratio of the output voltage of the port 2 to the incident voltage of the port 1; s22 is the ratio of the voltage reflected by port 2 to the incident voltage.
For step S1042, the calculating the remainder process includes calculating the remainder of the y parameter.
Aiming at step S1043, performing a meta-elimination process on the transverse topology matrix in sequence, determining a round topology matrix, and extracting a transmission zero, including: and extracting reflection zero points and transmission zero points of the corresponding S parameters of the wheel topology matrix, wherein the extracted reflection zero points and transmission zero points can be used for subsequent similar transformation processing.
For step S1044, performing a similar transformation on the wheel topology matrix according to the transmission zero point, to obtain a current coupling matrix corresponding to an actual topology.
It should be noted that, first, extracting the transmission zero and reflection zero of the coupling matrix requires extracting F(s), P(s), E(s),、/>This is three characteristic polynomials characterizing the filter S parameter response, and two ripple constants, calculated as follows:
（9）
（10）
wherein the formula is generally calculated as a numerical value, and the characteristic polynomial is innovatively extracted here forever.
Expanding A to:
here, polynomial coefficients with respect to s are required, where there is no variable s on the outermost two diagonals of a and there is a variable s on the inner diagonal. As can be seen from the knowledge of the linear algebra, . The specific steps of the calculation of the determinant |A| are as follows: the first row is removed by elimination, 1 is extracted by Yu Zishi, and then the same operation is performed on the last column, so s remains on the diagonal. />Also calculate +.>The calculation of the (a) is required to be performed on the basis of a round topology matrix or a transverse topology matrix, and for the transverse topology, the calculation is required to be performed by using the definition of a determinant, and because only diagonal lines and matrix peripheral elements of the transverse topology are not 0, the number of permutation and combination is limited, and the calculation of coefficients can be realized by writing a program according to the definition of the determinant. According to the round topology calculation, it is simpler, because MS1 represents the main coupling, and it is impossible to be 0, so s+jm11, jm12 can be eliminated by jMS1, then:
（11）
（12）
thus, an iteration is completed, and the following iterations are the same asThe iteration is completed when the elements on the secondary diagonal, i.e. the third diagonal, are all eliminated, jM NL The expression of (c) has the same zero point as P(s), where the coefficient of P(s) and the zero point are both extractable.
And S105, determining whether the optimization meets the standard or not according to the current S parameter, the current coupling matrix, the current target matrix and the preset target return loss.
In one embodiment provided herein, the current target matrix is determined by: determining a target S parameter according to a preset target return loss, a preset target transmission zero point and a preset target passband range, and determining a preset target coupling matrix according to the target S parameter; comparing the current coupling matrix with an actual topology to determine whether parasitic coupling exists in the coupling matrix but does not exist in the actual topology; if the parasitic coupling exists, adjusting the preset target coupling matrix according to a preset adjustment mode, and determining the current target matrix; and if the current target matrix does not exist, determining the preset target coupling matrix as the current target matrix.
Here, the current target matrix is a dynamic matrix, that is, the current target matrix corresponding to each optimization may be the same or different.
For example, referring to fig. 3, fig. 3 is a flowchart of a current target matrix determination process provided in the present application, where RL is return loss, wn is transmission zero, RL and wn are design parameters, me is an extracted wheel topology matrix,as shown in fig. 3, S301 is input RL, wn, me, M0; s302, solving a transmission zero point w0 and a parasitic zero point Wp of Me; s303, utilizing w0 to change Me similarity to M; s304, let- >=/>，/>FixingOptimization->The method comprises the steps of carrying out a first treatment on the surface of the S305, obtaining/>Transmission zero of->And isolating parasitic zero->The method comprises the steps of carrying out a first treatment on the surface of the S306, utilize [>,/>]RL is synthesized with->；S307，/>? The method comprises the steps of carrying out a first treatment on the surface of the If not, go to step S308, if yes, go to step S309; s308, let->；S309，/>Is the current target matrix.
Therefore, by adopting the dynamic target coupling matrix and determining the current target coupling matrix according to parasitic coupling, the coupling matrix can be accurately corrected when the parasitic coupling is strong, so that the accuracy of the filter parameter optimization result is improved.
In an embodiment provided in the present application, determining whether the current optimization meets the standard according to the current S parameter, the current coupling matrix, the current target matrix, and the preset target return loss includes:
s1051, determining a response error according to the current S parameter and the target return loss.
S1052, determining matrix errors according to the current coupling matrix and the current target matrix.
S1053, carrying out weighted summation processing according to the response error and the matrix error to determine a total error.
S1054, if the total error is smaller than the error threshold, determining that the optimization reaches the standard, otherwise, determining that the optimization does not reach the standard.
Here, the weight values corresponding to the response error and the matrix error may be the same or different. For example, the response error may be set to a weight value of 0.3 and the matrix error may be set to a weight value of 0.7.
And S106, if yes, determining the estimated optimal value as a target value, and outputting and ending optimization.
In the step, if the filter meets the standard, determining the estimated optimal value of each parameter to be optimized of the filter as a target value, and ending optimization.
In one embodiment provided in the present application, if the optimization method does not reach the standard, the optimization method further includes:
and S107, if the estimated optimal value and the corresponding coupling matrix are not up to standard, updating the reduced order model, and re-optimizing parameters of the filter by using the updated reduced order model.
The method specifically comprises the following steps: adding the estimated optimal value and the corresponding coupling matrix to the initial data set; changing a first-order Taylor expansion into a second-order Taylor expansion according to the initial data set, and correcting the coefficient of the Taylor expansion by utilizing the initial data set so as to update the reduced model; and re-optimizing parameters of the filter by using the updated reduced order model.
In this step, the parameter optimization of the filter is performed again by using the updated reduced-order model, and the updated reduced-order model is returned to step S103 as the reduced-order model in step S103.
By way of example, the update procedure includes adding 1 to the number of columns of the following matrix:
∆M=X∙A
x is a matrix of the fatted X vector (fatted X is the difference between X at each calculated point and X at the initial point). In addition, 1 matrix equation exists for every 1 element in the coupling matrix, so that n×n matrix equations are shared, when sampling points are less than n+n×n (n+1)/2, the method directly solves by adopting the kramer rule, when the sampling points are greater than n+n×n (n+1)/2, the least squares solution is solved, the first N elements of the solution A obtained represent gradients, and the last n×n (n+1)/2 elements correspond to elements in the black plug matrix.
It should be noted that, by using the estimated optimal value of the parameter to be adjusted of the filter, the reduced order model is updated in a manner similar to the translation of the reference origin in mathematic, so that 2n+1 times of optimization simulation are not needed each round.
For example, referring to fig. 4, fig. 4 is a schematic diagram of a filter response result provided in the present application, as shown in fig. 4, the upper diagram is an initial response result of a filter, and the lower diagram is a response result of an optimized filter, where parameters to be adjusted of the filter include 14 parameters, and the target is-21 dB, and through the present solution, 34 times (15 parameter acquisitions, 19 iterations) are co-simulated to reach the target requirement. Therefore, the optimization efficiency is effectively improved.
Therefore, the reduced order model constructed by the method and the device can effectively reduce the simulation times by introducing a penalty function and considering parasitic coupling in optimization, and further provide the optimization efficiency of the filter parameters.
Referring to fig. 5 and 6, fig. 5 is a schematic structural diagram of a filter optimizing apparatus according to an embodiment of the present application, and fig. 6 is a schematic structural diagram of a filter optimizing apparatus according to an embodiment of the present application. As shown in fig. 5, the optimizing apparatus 500 includes:
a first construction module 510, configured to determine an initial coupling matrix and n first coupling matrices according to an initial value of each parameter to be adjusted and n sets of linearly independent setting values of the parameter to be adjusted, and construct an initial data set using the initial value of each parameter to be adjusted and the n sets of linearly independent setting values and the initial coupling matrix and the n first coupling matrices; wherein n is the dimension of the parameter to be adjusted;
the second construction module 520 is configured to perform taylor expansion processing on the initial coupling matrix and all elements in the n first coupling matrices, determine coefficients of first-order taylor expansion by using the initial data set, and construct a reduced-order model;
The optimizing module 530 is configured to perform parameter optimization by using the reduced order model, so as to obtain an estimated optimal value of the parameter to be adjusted of the filter;
the simulation module 540 is configured to simulate to obtain a current S parameter of the filter according to the estimated optimal value of the parameter to be adjusted of the filter, and extract a current coupling matrix according to the current S parameter;
a judging module 550, configured to determine whether the optimization meets the standard according to the current S parameter, the current coupling matrix, the current target matrix, and a preset target return loss;
and an ending module 560, configured to determine the estimated optimal value as a target value if the estimated optimal value reaches the standard, and output and end the optimization.
Optionally, when the first construction module 510 is configured to determine an initial coupling matrix and n first coupling matrices according to an initial value of each parameter to be adjusted of the filter and n sets of linearly independent setting values of the parameter to be adjusted, the first construction module 510 is configured to:
according to the initial value of each parameter to be adjusted of the filter, determining the initial S parameter of the filter;
determining the initial coupling matrix according to the initial S parameter;
for n groups of linearly independent set values, determining the first S parameters corresponding to each set of the set values and the first coupling matrixes corresponding to the first S parameters respectively to obtain n first coupling matrixes.
Optionally, when the optimizing module 530 is configured to perform parameter optimization by using the reduced order model, obtain an estimated optimal value of the parameter to be adjusted of the filter, the optimizing module is configured to:
constructing a penalty function according to the n groups of linearly independent setting values, and introducing the penalty function into the reduced order model;
and carrying out minimum value solving processing on the reduced order model introduced with the penalty function by using a Gill-Murray Newton method, and determining the estimated optimal value of the parameters to be regulated of the filter according to the solved minimum value.
Optionally, when the simulation module 540 is configured to simulate to obtain a current S parameter of the filter according to the estimated optimal value of the parameter to be adjusted of the filter, and extract the current coupling matrix according to the current S parameter, the simulation module 540 is configured to:
inputting the estimated optimal value of the parameters to be adjusted of the filter into a solver, and determining the current S parameter of the filter;
sequentially removing loading phases and calculating remainder to the current S parameters to determine a transverse topology matrix;
sequentially performing element elimination treatment on the transverse topology matrix, determining a wheel type topology matrix, and extracting a transmission zero point;
And performing similar transformation on the wheel type topology matrix according to the transmission zero point to obtain a current coupling matrix corresponding to the actual topology.
Optionally, the optimizing device 500 further includes a determining module 570, where the determining module 570 is configured to:
determining a target S parameter according to a preset target return loss, a preset target transmission zero point and a preset target passband range, and determining a preset target coupling matrix according to the target S parameter;
comparing the current coupling matrix with an actual topology to determine whether parasitic coupling exists in the coupling matrix but does not exist in the actual topology;
if the parasitic coupling exists, adjusting the preset target coupling matrix according to a preset adjustment mode, and determining the current target matrix;
and if the current target matrix does not exist, determining the preset target coupling matrix as the current target matrix.
Optionally, when the determining module 550 is configured to determine whether the current optimization meets the standard according to the current S parameter, the current coupling matrix, the current target matrix, and the preset target return loss, the determining module 550 is configured to:
determining a response error according to the current S parameter and the preset target return loss;
Determining a matrix error according to the current coupling matrix and the current target matrix;
performing weighted summation processing according to the response error and the matrix error to determine a total error;
if the total error is smaller than the error threshold, determining that the optimization reaches the standard, otherwise, determining that the optimization does not reach the standard.
Optionally, the optimizing apparatus 500 further includes an updating module 580, and the updating module 580 is configured to:
adding the estimated optimal value and the corresponding coupling matrix to the initial data set;
changing a first-order Taylor expansion into a second-order Taylor expansion according to the initial data set, and correcting the coefficient of the Taylor expansion by utilizing the initial data set so as to update the reduced model;
and re-optimizing parameters of the filter by using the updated reduced order model.
Referring to fig. 7, fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 7, the electronic device 700 includes a processor 710, a memory 720, and a bus 730.
The memory 720 stores machine-readable instructions executable by the processor 710, when the electronic device 700 is running, the processor 710 communicates with the memory 720 through the bus 730, and when the machine-readable instructions are executed by the processor 710, the steps in the method embodiments shown in fig. 1, 3 and 4 can be executed, and the specific implementation can be referred to the method embodiments and will not be described herein.
The embodiment of the present application further provides a computer readable storage medium, where a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the steps in the method embodiments shown in fig. 1, fig. 3, and fig. 4 may be executed, and the specific implementation may refer to the method embodiments and will not be described herein.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the foregoing examples are merely specific embodiments of the present application, and are not intended to limit the scope of the present application, but the present application is not limited thereto, and those skilled in the art will appreciate that while the foregoing examples are described in detail, the present application is not limited thereto. Any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or make equivalent substitutions for some of the technical features within the technical scope of the disclosure of the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of optimizing a filter, the method comprising:
determining an initial coupling matrix and n first coupling matrices according to the initial value of each parameter to be adjusted of the filter and n groups of linearly independent setting values of the parameter to be adjusted, and constructing an initial data set by using the initial value of each parameter to be adjusted, the n groups of linearly independent setting values, the initial coupling matrix and the n first coupling matrices; wherein n is the dimension of the parameter to be adjusted;
Performing taylor expansion processing on all elements in the initial coupling matrix and the n first coupling matrices, determining a coefficient of first-order taylor expansion by using the initial data set, and constructing a reduced-order model;
performing parameter optimization by using the reduced order model to obtain an estimated optimal value of the parameters to be adjusted of the filter;
simulating to obtain the current S parameter of the filter according to the estimated optimal value of the parameter to be adjusted of the filter, and extracting the current coupling matrix according to the current S parameter;
determining whether the optimization meets the standard or not according to the current S parameter, the current coupling matrix, the current target matrix and a preset target return loss;
if yes, determining the estimated optimal value as a target value, and outputting and ending optimization.
2. The optimization method according to claim 1, wherein determining an initial coupling matrix and n first coupling matrices according to the initial value of each parameter to be adjusted of the filter and n sets of linearly independent setting values of the parameter to be adjusted comprises:
according to the initial value of each parameter to be adjusted of the filter, determining the initial S parameter of the filter;
determining the initial coupling matrix according to the initial S parameter;
For n groups of linearly independent set values, determining the first S parameters corresponding to each set of the set values and the first coupling matrixes corresponding to the first S parameters respectively to obtain n first coupling matrixes.
3. The optimization method according to claim 2, wherein the performing parameter optimization using the reduced order model to obtain the estimated optimal value of the parameter to be adjusted of the filter includes:
constructing a penalty function according to the n groups of linearly independent setting values, and introducing the penalty function into the reduced order model;
and carrying out minimum value solving processing on the reduced order model introduced with the penalty function by using a Gill-Murray Newton method, and determining the estimated optimal value of the parameters to be regulated of the filter according to the solved minimum value.
4. The optimization method according to claim 1, wherein simulating the current S parameter of the filter according to the estimated optimal value of the parameter to be adjusted of the filter, and extracting the current coupling matrix according to the current S parameter, comprises:
inputting the estimated optimal value of the parameters to be adjusted of the filter into a solver, and determining the current S parameter of the filter;
Sequentially removing loading phases and calculating remainder to the current S parameters to determine a transverse topology matrix;
sequentially performing element elimination treatment on the transverse topology matrix, determining a wheel type topology matrix, and extracting a transmission zero point;
and performing similar transformation on the wheel type topology matrix according to the transmission zero point to obtain a current coupling matrix corresponding to the actual topology.
5. The optimization method of claim 4, wherein the current target matrix is determined by:
determining a target S parameter according to a preset target return loss, a preset target transmission zero point and a preset target passband range, and determining a preset target coupling matrix according to the target S parameter;
comparing the current coupling matrix with an actual topology to determine whether parasitic coupling exists in the coupling matrix but does not exist in the actual topology;
if the parasitic coupling exists, adjusting the preset target coupling matrix according to a preset adjustment mode, and determining the current target matrix;
and if the current target matrix does not exist, determining the preset target coupling matrix as the current target matrix.
6. The optimization method according to claim 1, wherein the determining whether the current optimization meets the standard according to the current S parameter, the current coupling matrix, the current target matrix, and the preset target return loss comprises:
Determining a response error according to the current S parameter and the preset target return loss;
determining a matrix error according to the current coupling matrix and the current target matrix;
performing weighted summation processing according to the response error and the matrix error to determine a total error;
if the total error is smaller than the error threshold, determining that the optimization reaches the standard, otherwise, determining that the optimization does not reach the standard.
7. The optimization method according to claim 1, wherein if not, the optimization method further comprises:
adding the estimated optimal value and the corresponding coupling matrix to the initial data set;
changing a first-order Taylor expansion into a second-order Taylor expansion according to the initial data set, and correcting the coefficient of the Taylor expansion by utilizing the initial data set so as to update the reduced model;
and re-optimizing parameters of the filter by using the updated reduced order model.
8. An optimization device of a filter, characterized in that the optimization device comprises:
the first construction module is used for determining an initial coupling matrix and n first coupling matrices according to the initial value of each parameter to be regulated of the filter and n groups of linearly independent setting values of the parameter to be regulated, and constructing an initial data set by using the initial value of each parameter to be regulated, the n groups of linearly independent setting values, the initial coupling matrix and the n first coupling matrices; wherein n is the dimension of the parameter to be adjusted;
The second construction module is used for carrying out Taylor expansion processing on all elements in the initial coupling matrix and the n first coupling matrices, determining a first-order Taylor expansion coefficient by utilizing the initial data set and constructing a reduced-order model;
the optimization module is used for carrying out parameter optimization by utilizing the reduced order model to obtain an estimated optimal value of the parameters to be adjusted of the filter;
the simulation module is used for obtaining the current S parameter of the filter through simulation according to the estimated optimal value of the parameter to be adjusted of the filter, and extracting the current coupling matrix according to the current S parameter;
the judging module is used for determining whether the optimization meets the standard or not according to the current S parameter, the current coupling matrix, the current target matrix and the preset target return loss;
and the ending module is used for determining the estimated optimal value as a target value if the estimated optimal value reaches the standard, and outputting and ending the optimization.
9. An electronic device, comprising: a processor, a memory and a bus, said memory storing machine readable instructions executable by said processor, said processor and said memory communicating via said bus when the electronic device is running, said machine readable instructions when executed by said processor performing the steps of the optimization method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, performs the steps of the optimization method according to any of claims 1 to 7.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6135649A (en) * | 1998-03-09 | 2000-10-24 | Lucent Technologies Inc. | Method of modeling and analyzing electronic noise using Pade approximation-based model-reduction techniques |
| CN107526869A (en) * | 2017-07-18 | 2017-12-29 | 电子科技大学 | A kind of numerical method based on function approximation self-adaptation three-dimensional microwave tube input and output window model reduction |
| CN109063374A (en) * | 2018-08-31 | 2018-12-21 | 中国地质大学（武汉） | A kind of coupling matrix extracting method based on parameter optimization, equipment and storage equipment |
| CN110765651A (en) * | 2019-11-12 | 2020-02-07 | 中国电子科技集团公司第二十九研究所 | Modeling and intelligent design method of microstrip direct coupling filter |
| CN110829485A (en) * | 2019-10-10 | 2020-02-21 | 国网湖南综合能源服务有限公司 | LCL filter parameter and control parameter global optimization design method, system and medium based on particle swarm optimization |
| CN114896778A (en) * | 2022-05-05 | 2022-08-12 | 西安电子科技大学 | Filter coupling matrix extraction method based on optimization |
| CN116204759A (en) * | 2022-12-27 | 2023-06-02 | 电子科技大学 | Extraction method of filter coupling matrix and related device |
-
2024
- 2024-01-11 CN CN202410038802.5A patent/CN117556773B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6135649A (en) * | 1998-03-09 | 2000-10-24 | Lucent Technologies Inc. | Method of modeling and analyzing electronic noise using Pade approximation-based model-reduction techniques |
| CN107526869A (en) * | 2017-07-18 | 2017-12-29 | 电子科技大学 | A kind of numerical method based on function approximation self-adaptation three-dimensional microwave tube input and output window model reduction |
| CN109063374A (en) * | 2018-08-31 | 2018-12-21 | 中国地质大学（武汉） | A kind of coupling matrix extracting method based on parameter optimization, equipment and storage equipment |
| CN110829485A (en) * | 2019-10-10 | 2020-02-21 | 国网湖南综合能源服务有限公司 | LCL filter parameter and control parameter global optimization design method, system and medium based on particle swarm optimization |
| CN110765651A (en) * | 2019-11-12 | 2020-02-07 | 中国电子科技集团公司第二十九研究所 | Modeling and intelligent design method of microstrip direct coupling filter |
| CN111832195A (en) * | 2019-11-12 | 2020-10-27 | 中国电子科技集团公司第二十九研究所 | Modeling and intelligent design method of microstrip direct coupling filter |
| CN114896778A (en) * | 2022-05-05 | 2022-08-12 | 西安电子科技大学 | Filter coupling matrix extraction method based on optimization |
| CN116204759A (en) * | 2022-12-27 | 2023-06-02 | 电子科技大学 | Extraction method of filter coupling matrix and related device |
Non-Patent Citations (2)
| Title |
|---|
| 周鑫宇: "耦合腔体滤波器高阶拓扑结构的研究", 《中国优秀硕士学位论文全文数据库信息科技辑》, 30 June 2023 (2023-06-30), pages 1 - 72 * |
| 戴卓: "基于BBO与ACO的混合优化算法及其在滤波器设计中的应用", 《中国优秀硕士学位论文全文数据库信息科技辑》, 15 September 2021 (2021-09-15), pages 1 - 81 * |
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